Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet

Simanungkalit, Julius Rinaldi; Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Puspitasari, Novianti; Wati, Masna

doi:10.33096/ilkom.v12i1.521.32-38

Cited by 6 publications

(8 citation statements)

References 6 publications

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“…The test results, the parameters of the BPNN algorithm with a data ratio of 4:1, the architectural model of 5-10-10-10-1, the learning function trainlm , the learning rate of 0.5, the error tolerance of 0.01 and the epoch of 1000 have obtained good accuracy with the mean value. square error (MSE) of 0.00015464 [17].…”

Section: Artificialmentioning

confidence: 99%

Identification of Color and Texture of Ripe Passion Fruit with Perceptron Neural Network Method

Siswanto

Sungkar

Anif

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Research using artificial neural network methods has been developed as a tool that can help human tasks, one of which is for passion fruit UMKM entrepreneurs. The problem so far that has been faced by UMKM entrepreneurs of passion fruit is that it is difficult to identify ripe passion fruit with sweet and sour taste, because there are 6 colors of passion fruit and the color of passion fruit skin is visually slightly different, as well as the texture of maturity. The main purpose of this study was to identify the color structure and texture of the ripeness of passion fruit, in order to recognize the color and texture of the ripeness of passion fruit which is good for processing into syrup, jam, jelly, juice, passion fruit juice powder by entrepreneurs of UMKM of passion fruit. This study empirically tested the color and texture of the ripeness of 10 passion fruit using the perceptron artificial neural network learning method. The data is obtained from an image that will be entered into the program. The results of the identification process using the perceptron artificial neural network from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.8 and 500 epoch iterations and producing an accuracy of 80%.

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Section: Artificialmentioning

confidence: 99%

Identification of Color and Texture of Ripe Passion Fruit with Perceptron Neural Network Method

Siswanto

Sungkar

Anif

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Section: Pendahuluanunclassified

“…Metode JST merupakan metode yang sangat baik untuk mendapatkan hasil peramalan yang lebih baik (Aini et al, 2019;Haviluddin & Dengen, 2016;Ma et al, 2016;Majhi et al, 2014;Purnawansyah & Haviluddin, 2014;Sakinah et al, 2018;Simanungkalit et al, 2020;Syafiq et al, 2020). Model akurasi peramalan terbaik yang digunakan dalam JST adalah MSE Simanungkalit et al, 2020). Hal inilah yang menjadi dasar penelitian ini menerapkan metode Backpropagation Neural Network (BPNN) dalam meramalkan data pelayanan service mobil (after-sale) pada tahun berikutnya.…”

Section: Pendahuluanunclassified

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Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

Puspitasari

Haviluddin

Kustiawan

et al. 2021

JISKa

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The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.

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“…The test results, the parameters of the BPNN algorithm with a data ratio square error (MSE) of 0.00015464 [14].…”

Section: Introductionmentioning

confidence: 99%

Identify the Color and Shape of Eggplant Using Back Propagation Method

Siswanto

Yuhefizar

Anif

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Currently, artificial neural networks are being developed as a tool that can help human tasks. The main purpose of this study is to identify the structure of an eggplant, in order to distinguish the type of eggplant. This study empirically tested the shape and color of several eggplants using the back propagation neural network learning method. The data is obtained from an image that will be entered into the program. The data used in the identification process are two photos containing two types of eggplant, the first eggplant is green and round and the next eggplant is purple and oval. The results of the identification process using this back propagation from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.7 and epoch iterations of 500 and producing an accuracy of 73.33%.

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Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet

Cited by 6 publications

References 6 publications

Identification of Color and Texture of Ripe Passion Fruit with Perceptron Neural Network Method

Identification of Color and Texture of Ripe Passion Fruit with Perceptron Neural Network Method

Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

Identify the Color and Shape of Eggplant Using Back Propagation Method

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